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Capella University *

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FP-2001

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Statistics

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Apr 3, 2024

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Data Analysis and Application Grades Jessica Olivares Capella University 1

Data Analysis and Application The fictional data represents a teacher’s recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of about 35 students (N = 105). Data Analysis Plan There are four variables used to find out if there is a correlation between the variables. The variables are Quiz 1 shows number of correct answers, is categorical and is an independent variable . GPA shows previous grade point average, is and is continuous. Total number of points earned is categorical and is an independent variable. Final grade is categorical. Is there a correlation between Quiz 1 (x) and Final grade (Y)? The null hypothesis shows is no correlation between Quiz 1 and Final grade. Alternate Hypothesis would show correlation between Quiz 1 and Final Grade Testing Assumptions Descriptive Statistics quiz1 gpa total Final Valid 105 105 105 105 Mean 7.467 2.862 100.086 61.838 Std. Deviation 2.481 0.713 13.427 7.635 Skewness -0.851 -0.220 -0.757 -0.341 Std. Error of Skewness 0.236 0.236 0.236 0.236 Kurtosis 0.162 -0.688 1.146 -0.277 Std. Error of Kurtosis 0.467 0.467 0.467 0.467 Minimum 0.000 1.080 54.000 40.000 Maximum 10.000 4.000 123.000 75.000 The data chart shows that for this test the alternate hypothesis rejects the null. 2

Results and Interpretation Descriptive Statistics quiz1 gpa total final Valid 105 105 105 105 Mean 7.467 2.862 100.086 61.838 Skewness -0.851 -0.220 -0.757 -0.341 Std. Error of Skewness 0.236 0.236 0.236 0.236 Kurtosis 0.162 -0.688 1.146 -0.277 Std. Error of Kurtosis 0.467 0.467 0.467 0.467 Skewness & Kurtosis falls within the normal range, the assumption is met. N=105-2 degrees of freedom. Pearson's Correlations Variable quiz1 gpa total final 1. quiz1 Pearson's r — p-value — Effect size (Fisher's z) — SE Effect size — 2. gpa Pearson's r 0.152 — p-value 0.121 — Effect size (Fisher's z) 0.153 — SE Effect size 0.099 — 3. total Pearson's r 0.797 0.318 — p-value < .001 < .001 — Effect size (Fisher's z) 1.092 0.329 — SE Effect size 0.099 0.099 — 4. final Pearson's r 0.499 0.379 0.875 — p-value < .001 < .001 < .001 — Effect size (Fisher's z) 0.549 0.399 1.353 — SE Effect size 0.099 0.099 0.099 — 3

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The correlation matrix shows the relationship of the variables. The lowest magnitude correlation is GPA & QUIZ 1. T he Matrix also shows the value of the correlation coefficient 0.152 The intercorrelation matrix between multiple variables including the -2 degrees of freedom is 103. Correlation coefficient, p- values is 0 .121. The effect size is another way to represent the r=0.10 medium is r=0.30 large correlations is r=0.50. Effect size is also your magnitude. The effect size does not reject the hypothesis. Second, the highest magnitude correlation in the intercorrelation matrix is the Tolal Effect size at 1.353. with N-2 degrees of freedom is 103. Correlation coefficient p value and effects size,  Third, o Third, report the correlation between GPA and final, including degrees of freedom, correlation coefficient, p-value, and effect size. Interpret the effect size. Analyze the correlation in terms of the null hypothesis.  Interpret statistical results against the null hypothesis, and state whether it is accepted or rejected. Statistical Conclusions 1. Provide a brief summary of your analysis and the conclusions drawn. 2. Analyze the limitations of the statistical test. A limitations of correlational research itself. 4

3. Provide any possible alternate explanations for the findings and potential areas for future exploration. The weather could have impacted the stress of why the students performed Application Think of your own field of study. Describe how this type of analysis might be used in your field. Relationship between person specific treatment goals and outcomes. 1. Analyze what the value and potential implication of such an analysis would be. 5